Modeling and Control of Nonlinear Dynamic Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".
Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 2359
Special Issue Editors
Interests: modeling and control of nonlinear dynamic systems; adaptive and intelligent control theory; soft-computing and machine intelligence
Special Issues, Collections and Topics in MDPI journals
2. Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Interests: control systems; power systems; power electronics; electric machines; smart grid; renewable and distributed energy resources; power quality and energy management; real-time simulations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Energy systems are one of the most complex physical dynamic systems, which can be substantive by sets of differential and algebraic equations. Their complexity not only emanates from the large number of equations describing the system’s dynamic behavior, but also from the presence of nonlinearities and other unpredictable factors.
Modeling and control of nonlinear dynamic systems is an active field of research. This Special Issue focuses on the latest developments in modeling and control of nonlinear dynamic systems and their application to energy systems. Techniques range from classical modeling and control approaches to intelligent techniques such as neural network, fuzzy logic, neurofuzzy, evolutionary computation, self-organizing systems, machine learning, and multi-agent systems, to name a few. Potential topics include but are not limited to the following:
- Modeling of nonlinear dynamic energy systems;
- Energy system identification;
- Linear control systems of energy systems;
- State feedback control of energy systems;
- Nonlinear control design of energy systems;
- Robust control of energy systems;
- Adaptive control of energy systems;
- Sliding-mode control of energy systems;
- Nonlinear model-based control of energy systems;
- Intelligent control of energy systems;
- Reinforcement learning-based control of energy systems;
- Deep learning-based control of energy systems;
- Machine learning for energy systems;
- Stability analysis of energy systems;
- Optimization of operation of energy systems;
- Application of artificial intelligence for modeling and control of energy systems;
- New trends in modeling and control of nonlinear dynamic energy systems.
Dr. Hicham Chaoui
Dr. Mohamad Alzayed
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- Optimal control
- Robust control
- Adaptive control
- Nonlinear dynamics
- Neural network
- Fuzzy logic
- Neurofuzzy
- Deep learning
- Intelligent optimization
- Stability analysis
